A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis

Melín, Patricia; Miramontes, Ivette; Prado-Arechiga, German

doi:10.1016/j.eswa.2018.04.023

Cited by 98 publications

(32 citation statements)

References 19 publications

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“…Li et al (2018) used Den-seNet-121 and DenseNet-RNN were two deep learning models utilized to analyze the infections in ChestX-Ray14, where DenseNet-121 getting a sum of 74.5% and DenseNet-RNN was 75.1% to recognizing Pneumonia. Rajpurkar et al (2017) were introduced by taking 121 layers to identify one of the 14 infections at 76.8% of accuracy of the pneumonic class from the others; likewise this model gives a heatmap for possible localization that depends on the forecast done by the convolutional neural network, and more study can be found by applying machine learning and deep learning algorithm to analyze the X-ray and CT images (Basu et al 2020;Pavithra et al 2015;Ozkaya et al 2020;Santos and Melin 2020;Tolga et al 2020;Ramírez et al 2019;Miramontes et al 2018;Melin et al 2018;Kermany, et al 2018b, a;Ayan, and Ü nver, 2019;Varshni et al 2019;Wang et al 2017;Togaçar et al 2019;Jaiswal, et al 2019;Sirazitdinov, et al 2019;Behzadi-khormouji et al 2020;Stephen et al 2019;Xu et al 2020;Shan et al 2020).…”

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confidence: 99%

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models. Keywords COVID-19 Á X-ray images Á CT scan Á CNN Á VGG-16 Á Inception_V2 Á Decision tree 1 Introduction The first case of the virus became exposed in Wuhan city of China in November 2019, there are 1,100,000 peoples living in this city and it interfaces numerous urban communities of China. The outbreak of atypical and individual-to-individual transmissible pneumonia brought about by the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) has caused a worldwide. There have been in excess of 26,000,000 confirmed cases of the corona virus disease (COVID-19) on the globe, as of April 23, 2020. As indicated by the WHO, 16-21% of individuals with the infection have gotten seriously sick with a Communicated by Valentina E. Balas.

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confidence: 99%

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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“…ere are several studies in the literature on the detection and classification of hypertension. Among them, Melin et al used the neural network and fuzzy inference system to classify the hypertension type based on the age, risk factors, and behavior of the blood pressure in a period of 24 h. ey obtained the 98% classification performance as the maximum with their method [6]. Singh et al proposed a new method called rule extraction from the support vector machine to diagnose hypertension in diabetes mellitus patients.…”

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confidence: 99%

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Polat

2020

Mathematical Problems in Engineering

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Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.

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“…A particular modular neural network is utilized to give information to the classifier, this information is the systolic and diastolic weights and is given by every patient in the 24 h monitoring and this data are the inputs for the module of the modular neural network. In this phase, the neural network learns and models the data to finally give a result, which will be the inputs to the fuzzy system to classify in the most ideal way and provide a correct analysis and help the cardiologist to the precise control and diagnosis of each patient [31][32][33][34][35].…”

Section: Problem Statement and Proposed Methodsmentioning

confidence: 99%

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Guzman

Miramontes

Melín

et al. 2019

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The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.

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A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis

Cited by 98 publications

References 19 publications

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

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